用知识图谱和大模型实现动态个性化消息生成
Leveraging Knowledge Graphs and LLMs for Context-Aware Messaging
- 基于知识图谱关联人物、地点、事件，动态重构沟通内容
- 在医疗/教育/招聘领域消息接受率分别达42%/53%/78%
- 适合需要精准受众沟通的场景，如智能客服与职业推荐
个性化消息在医疗、教育和职场互动中至关重要。本文提出一种框架，利用知识图谱（KG）动态重写文本信息，整合个体与情境数据。知识图谱将个人、地点、事件作为关键节点，将消息中提及的实体链接至对应节点。通过提取偏好、职业角色、文化规范等信息，结合原文本输入大语言模型（LLM），生成个性化回复。该框架在多个领域表现优异：医疗领域消息接受率达42%，教育领域为53%，专业招聘领域高达78%。通过融合实体链接、事件检测与语言建模，提供结构化且可扩展的上下文感知通信方案，支持多领域高级应用。
原文摘要 · Abstract (English)
Personalized messaging plays an essential role in improving communication in areas such as healthcare, education, and professional engagement. This paper introduces a framework that uses the Knowledge Graph (KG) to dynamically rephrase written communications by integrating individual and context-specific data. The knowledge graph represents individuals, locations, and events as critical nodes, linking entities mentioned in messages to their corresponding graph nodes. The extraction of relevant information, such as preferences, professional roles, and cultural norms, is then combined with the original message and processed through a large language model (LLM) to generate personalized responses. The framework demonstrates notable message acceptance rates in various domains: 42% in healthcare, 53% in education, and 78% in professional recruitment. By integrating entity linking, event detection, and language modeling, this approach offers a structured and scalable solution for context-aware, audience-specific communication, facilitating advanced applications in diverse fields.
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